Ronald Denaux

Integrating open user modeling and learning content management for the semantic web

The paper describes an ontology-based approach for integrating interactive user modeling and learning content management to deal with typical adaptation problems, such as cold start and dynamics of the users knowledge, in the context of the Semantic Web. An integrated OntoAIMS system is presented and its viability discussed based on user studies. The work demonstrates some novel aspects, such as (a) ontological approach for integration of methods for eliciting and utilizing of user models; (b) improved adaptation functionality resulted from that integration, validated with real users; (c) support of interoperability and reusability of adaptive components.

ROO: involving domain experts in authoring OWL ontologies

This demonstration presents ROO, a tool that facilitates domain experts definition of ontologies in OWL by allowing them to author the ontology in a controlled natural language called Rabbit. ROO guides users through the ontology construction process by following a methodology geared towards domain experts involvement in ontology authoring, and exploiting intelligent user interfaces techniques. An experimental study with ROO was conducted to examine the usability and usefulness of the tool, and the quality of the resultant ontologies. The findings of the study will be presented in a full paper at the ISWC08 research track [2].

Supporting domain experts to construct conceptual ontologies: A holistic approach

Abstract A recent trend in ontology engineering research aims at encouraging the active participation of domain experts in the ontology creation process. Ontology construction methodologies together with appropriate tools and technologies, such as controlled natural languages, semantic wikis, intelligent user interfaces and social computing, are being proposed to enable the direct input from domain experts and to minimize the dependency on knowledge engineers at every step of ontology development. The time is ripe for consolidating methodological and technological advancements to create intuitive ontology engineering tools which can make Semantic Web technologies usable by a wide variety of people without formal knowledge engineering skills. A novel, holistic approach to facilitate the involvements of domain experts in the ontology authoring process is presented here. It integrates (i) an ontology construction methodology, (ii) the use of a controlled natural language, and (iii) appropriate tool support. The integrated approach is illustrated with the design, implementation and evaluation of ROO – a unique ontology authoring tool which combines intelligent techniques to assist domain experts in constructing ontologies. The benefits and limitations of the proposed approach are analyzed based on user studies with ROO. A broader discussion is provided pointing at issues to be taken into account when assisting the involvement of domain experts in ontology construction.

Rabbit to OWL: ontology authoring with a CNL-based tool

Recent work on ontology engineering has seen the adoption of controlled natural languages to ease the process of ontology authoring. However, CNL-based tools still require good knowledge engineering skills to be used efficiently. In this paper presents ROO, an ontology authoring tool that has been designed to cater for the needs of domain experts with little or no ontology engineering experience. ROO combines a CNL-based interface with appropriate tool support based on an ontology construction methodology. We focus on how this tool support is provided in ROO by using and implementing novel aspects of the Rabbit controlled natural language and we refer to an evaluation study that provides empirical evidence in support of using CNL-based techniques to assist ontology authors.

Interactive ontology-based user knowledge acquisition: a case study

On the Semantic Web personalization technologies are needed to deal with user diversity. Our research aims at maximising the automation of acquisition of user knowledge, thus providing an effective solution for multi-faceted user modeling. This paper presents an approach to eliciting a users conceptualization by engaging in an ontology-driven dialog. This is implemented as an OWL-based domain-independent diagnostic agent. We show the deployment of the agent in a use case for personalized management of learning content, which has been evaluated in three studies with users. Currently, the system is being deployed in a cultural heritage domain for personalized recommendation of museum resources.

An approach for ontology-based elicitation of user models to enable personalization on the semantic web

A novel framework for eliciting a users conceptualization based on an ontology-driven dialog is presented here. It has been integrated in an RDF/OWL-based architecture of an adaptive learning content management system. The implemented framework is illustrated with an application scenario to deal with the cold start problem and to enable tailoring the systems behavior to the needs of each individual user.

A priori ontology modularisation in ill-defined domains

Modularisation is crucial to create re-usable and manageable ontologies. The modularisation is usually performed a posteriori, i.e. after the ontology is developed, and has been applied mainly to well-structured domains. With the increasing popularity of social media, Semantic web technologies are moving towards ill-defined domains that involve cognitively-complex processes carried out by humans and require tacit knowledge (e.g. decision-making, sensemaking, interpersonal communication, negotiating, motivating). In such domains, a priori modularisation can enable ontology creation to handle the complexity and the dynamic nature of knowledge. This paper outlines an a priori modularisation methodology for multi-layered development of ontologies in ill-defined domains, including an upper ontology layer, high-level and reusable domain layers, and case-specific layers. The methodology is being applied in several use cases in two EU projects -- Dicode and ImREAL.

Employing linked data and dialogue for modelling cultural awareness of a user

Intercultural competence is an essential 21st Century skill. A key issue for developers of cross-cultural training simulators is the need to provide relevant learning experience adapted to the learnerfis abilities. This paper presents a dialogic approach for a quick assessment of the depth of a learners current intercultural awareness as part of the EU ImREAL project. To support the dialogue, Linked Data is seen as a rich knowledge base for a diverse range of resources on cultural aspects. This paper investigates how semantic technologies could be used to: (a) extract a pool of concrete culturally-relevant facts from DBpedia that can be linked to various cultural groups and to the learner, (b) model a learners knowledge on a selected set of cultural themes and (c) provide a novel, adaptive and user-friendly, user modelling dialogue for cultural awareness. The usability and usefulness of the approach is evaluated by CrowdFlower and Expert Inspection.

Using DBpedia as a Knowledge Source for Culture-Related User Modelling Questionnaires

In the culture domain, questionnaires are often used to obtain profiles of users for adaptation. Creating questionnaires requires subject matter experts and diverse content, and often does not scale to a variety of cultures and situations. This paper presents a novel approach that is inspired by crowdwisdom and takes advantage of freely available structured linked data. It presents a mechanism for extracting culturally-related facts from DBpedia, utilised as a knowledge source in an interactive user modelling system. A user study, which examines the system usability and the accuracy of the resulting user model, demonstrates the potential of using DBpedia for generating culture-related user modelling questionnaires and points at issues for further investigation.

K-CAP2017 Satellites: Workshops and Tutorials

ACM Reference Format: Carlos Badenes, Ronald Denaux, Martine De Vos, Daniel Garijo, Jose Manuel Gomez-Perez, Agnieszka Lawrynowicz, Pasquale Lisena, Raul Palma, Raphaël Troncy, and Daniel Vila. 2017. K-CAP2017 Satellites Workshops and Tutorials. In K-CAP 2017: K-CAP 2017: Knowledge Capture Conference CD-ROM, December 4–6, 2017, Austin, TX, USA. ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3148011.3188410